{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3046eed9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "沪深300 460\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import requests\n",
    "import json\n",
    "\n",
    "url = 'https://push2his.eastmoney.com/api/qt/stock/kline/get?cb=jQuery351016702133557143928_1757311398446&secid=1.000300&ut=fa5fd1943c7b386f172d6893dbfba10b&fields1=f1%2Cf2%2Cf3%2Cf4%2Cf5%2Cf6&fields2=f51%2Cf52%2Cf53%2Cf54%2Cf55%2Cf56%2Cf57%2Cf58%2Cf59%2Cf60%2Cf61&klt=101&fqt=1&beg=0&end=20500101&smplmt=460&lmt=1000000&_=1757311398447'\n",
    "\n",
    "repsonse = requests.get(url)\n",
    "text = repsonse.text\n",
    "# print(repsonse.text)\n",
    "# print(repsonse.status_code)\n",
    "\n",
    "\"\"\"\n",
    "jQuery35109392514059566558_1756826314307({\"rc\":0,\"rt\":17,\"svr\":181669694,\"lt\":1,\"full\":0,\"dlmkts\":\"\",\"data\":{\"code\":\"000300\",\"market\":1,\"name\":\"沪深300\",\"decimal\":2,\"dktotal\":5021,\"preKPrice\":3941.42,\"klines\":[\"2025-03-12,3946.86,3927.23,3953.61,3921.47,156732190,309627267364.80,0.82,-0.36,-14.19,0.48\",\"2025-03-13,3925.74,3911.58,3939.29,3897.38,\n",
    "\"\"\"\n",
    "\n",
    "match = re.search(r'\\((\\{.*\\})\\)', text)\n",
    "\n",
    "json_str = match.group(1) # 获取括号中的内容\n",
    "data = json.loads(json_str)\n",
    "\n",
    "gupiao_name = data['data']['name']\n",
    "gupiao_size = len(data['data']['klines'])\n",
    "\n",
    "print(gupiao_name, gupiao_size)\n",
    "\n",
    "# for line in data['data']['klines']:\n",
    "#     print(line.split(','))\n",
    "\n",
    "data_list = [line.split(',') for line in data['data']['klines']]\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3950f71f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               open    close     high      low     volume           amount  \\\n",
      "date                                                                         \n",
      "2005-01-04   994.77   982.79   994.77   980.66    7412868    4431977400.00   \n",
      "2005-01-19   974.33   967.21   974.33   965.26    6338091    3427951300.00   \n",
      "2005-02-03  1005.56   993.22  1014.19   992.16   16974539   10057310300.00   \n",
      "2005-03-01  1039.35  1035.93  1042.74  1031.17    9962090    6288790700.00   \n",
      "2005-03-16  1011.85  1003.07  1012.20   995.97   10622782    7153745900.00   \n",
      "...             ...      ...      ...      ...        ...              ...   \n",
      "2025-07-11  4011.47  4014.81  4065.94  4010.11  262052773  443780794880.40   \n",
      "2025-07-28  4128.20  4135.82  4146.12  4112.97  222759815  406792873973.30   \n",
      "2025-08-12  4123.58  4143.83  4153.29  4121.22  177287823  383088423330.00   \n",
      "2025-08-27  4458.84  4386.13  4494.91  4386.13  333133898  785098482973.50   \n",
      "2025-09-09  4448.74  4436.26  4472.09  4412.69  221375460  562861103958.40   \n",
      "\n",
      "           amplitude change_pct change_val turnover  \n",
      "date                                                 \n",
      "2005-01-04      0.00       0.00       0.00     0.02  \n",
      "2005-01-19      0.93      -0.77      -7.48     0.02  \n",
      "2005-02-03      2.19      -1.36     -13.69     0.05  \n",
      "2005-03-01      1.11      -0.39      -4.06     0.03  \n",
      "2005-03-16      1.60      -1.03     -10.45     0.03  \n",
      "...              ...        ...        ...      ...  \n",
      "2025-07-11      1.39       0.12       4.79     0.80  \n",
      "2025-07-28      0.80       0.21       8.66     0.68  \n",
      "2025-08-12      0.78       0.52      21.32     0.54  \n",
      "2025-08-27      2.44      -1.49     -66.46     1.02  \n",
      "2025-09-09      1.33      -0.70     -31.31     0.68  \n",
      "\n",
      "[460 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(data_list)\n",
    "df.columns = [\"date\", \"open\", \"close\", \"high\", \"low\", \n",
    "           \"volume\", \"amount\", \"amplitude\", \"change_pct\", \n",
    "           \"change_val\", \"turnover\"]\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df.set_index('date', inplace=True)\n",
    "df.to_csv(f'{gupiao_name}-{gupiao_size}.csv', index=False)\n",
    "\n",
    "print(df)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e6b4d035",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          date     open    close     high      low     volume  \\\n",
      "0   2005-01-04   994.77   982.79   994.77   980.66    7412868   \n",
      "1   2005-01-19   974.33   967.21   974.33   965.26    6338091   \n",
      "2   2005-02-03  1005.56   993.22  1014.19   992.16   16974539   \n",
      "3   2005-03-01  1039.35  1035.93  1042.74  1031.17    9962090   \n",
      "4   2005-03-16  1011.85  1003.07  1012.20   995.97   10622782   \n",
      "..         ...      ...      ...      ...      ...        ...   \n",
      "455 2025-07-10  3993.42  4010.02  4032.98  3992.52  209778904   \n",
      "456 2025-07-25  4145.32  4127.16  4150.50  4117.80  273594963   \n",
      "457 2025-08-11  4110.29  4122.51  4134.25  4103.61  190443716   \n",
      "458 2025-08-26  4453.35  4452.59  4476.80  4435.53  270621395   \n",
      "459 2025-09-08  4467.17  4462.98  4482.76  4438.07  213298875   \n",
      "\n",
      "              amount amplitude change_pct change_val turnover  \n",
      "0      4431977400.00      0.00       0.00       0.00     0.02  \n",
      "1      3427951300.00      0.93      -0.77      -7.48     0.02  \n",
      "2     10057310300.00      2.19      -1.36     -13.69     0.05  \n",
      "3      6288790700.00      1.11      -0.39      -4.06     0.03  \n",
      "4      7153745900.00      1.60      -1.03     -10.45     0.03  \n",
      "..               ...       ...        ...        ...      ...  \n",
      "455  347836703626.30      1.01       0.47      18.62     0.64  \n",
      "456  430445513470.90      0.79      -0.53     -21.88     0.84  \n",
      "457  360685826235.00      0.75       0.43      17.54     0.58  \n",
      "458  627950450229.20      0.92      -0.37     -16.63     0.83  \n",
      "459  579936436812.60      1.00       0.06       2.66     0.65  \n",
      "\n",
      "[460 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(data_list)\n",
    "df.columns = [\"date\", \"open\", \"close\", \"high\", \"low\", \n",
    "           \"volume\", \"amount\", \"amplitude\", \"change_pct\", \n",
    "           \"change_val\", \"turnover\"]\n",
    "df['date'] = pd.to_datetime(df['date']) # 将日期列转换为时间格式\n",
    "# df.set_index('date', inplace=True) # 将日期列设置为索引\n",
    "df.to_csv(f'{gupiao_name}-{gupiao_size}_date.csv', index=False)\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eddd647e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       close     volume\n",
      "0     982.79    7412868\n",
      "1     967.21    6338091\n",
      "2     993.22   16974539\n",
      "3    1035.93    9962090\n",
      "4    1003.07   10622782\n",
      "..       ...        ...\n",
      "454  3960.07  187213671\n",
      "455  4010.02  209778904\n",
      "456  4127.16  273594963\n",
      "457  4122.51  190443716\n",
      "458  4452.59  270621395\n",
      "\n",
      "[459 rows x 2 columns]\n",
      "(459, 2)\n",
      "0       967.21\n",
      "1       993.22\n",
      "2      1035.93\n",
      "3      1003.07\n",
      "4       942.20\n",
      "        ...   \n",
      "454    4010.02\n",
      "455    4127.16\n",
      "456    4122.51\n",
      "457    4452.59\n",
      "458    4462.98\n",
      "Name: close, Length: 459, dtype: float64\n",
      "(459,)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('沪深300-460.csv')\n",
    "\n",
    "\"\"\"\n",
    "因为处于学习阶段，因此我先用最基础的算法思路来预测股票价格\n",
    "因此就先选几个用于预测涨幅 open、close、high、low、volume、amount 这几个指标\n",
    "\n",
    "算法的话就用最简单的线性回归模型\n",
    "\"\"\"\n",
    "\n",
    "# data = data[['open', 'close', 'high', 'low', 'volume', 'amount']].values # 获取数据 // 转换为 numpy 数组\n",
    "# print(data)\n",
    "# print(data.shape) # 查看数据形状\n",
    "\n",
    "# 输入特征：今天收盘价、成交量\n",
    "X = data[['close','volume']].iloc[:-1]\n",
    "print(X)\n",
    "print(X.shape)\n",
    "\n",
    "# 预测目标：明天的收盘价\n",
    "y = data['close'].shift(-1).dropna()  # 预测目标：明天的收盘价 // shift(-1) 向下移动一行，dropna() 删除空值\n",
    "print(y)\n",
    "print(y.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28f497ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "权重： [9.72365248e-01 1.78945627e-07]\n",
      "偏置： 78.73046051125448\n",
      "模型得分： 0.9669192647306135\n",
      "预测明天收盘价： [4456.7007537]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda3\\envs\\main\\Lib\\site-packages\\sklearn\\utils\\validation.py:2749: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X, y) # 用输入特征 X 训练模型，预测目标 y // fit() 方法用来训练模型\n",
    "\n",
    "print(\"权重：\", model.coef_) # 查看模型的权重 // coef_ 属性用来查看模型权重\n",
    "print(\"偏置：\", model.intercept_) # 查看模型的偏置 // intercept_ 属性用来查看模型偏置\n",
    "\n",
    "print(\"模型得分：\", model.score(X, y)) # 查看模型的得分 // score() 方法用来查看模型得分, 1.0 表示模型效果最好\n",
    "\n",
    "print(\"预测明天收盘价：\", model.predict([X.iloc[-1].values])) # 预测明天的收盘价 // predict() 方法用来预测 // X.iloc[-1].values 表示最后一行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0c7a187d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: Loss 163.3998, w 2.0868, b 0.0496\n",
      "Epoch 50: Loss 244.5617, w 4.2216, b -0.0285\n",
      "Epoch 100: Loss 269.2376, w 4.4599, b -0.4859\n",
      "Epoch 150: Loss 277.8671, w 4.5668, b -0.9695\n",
      "Epoch 200: Loss 285.4590, w 4.6646, b -1.4550\n",
      "Epoch 250: Loss 293.1246, w 4.7617, b -1.9406\n",
      "Epoch 300: Loss 300.9421, w 4.8589, b -2.4262\n",
      "Epoch 350: Loss 308.9172, w 4.9560, b -2.9118\n",
      "Epoch 400: Loss 317.0501, w 5.0531, b -3.3974\n",
      "Epoch 450: Loss 325.3409, w 5.1502, b -3.8830\n",
      "Epoch 500: Loss 333.7896, w 5.2473, b -4.3686\n",
      "Epoch 550: Loss 342.3962, w 5.3445, b -4.8542\n",
      "Epoch 600: Loss 351.1607, w 5.4416, b -5.3398\n",
      "Epoch 650: Loss 360.0831, w 5.5387, b -5.8254\n",
      "Epoch 700: Loss 369.1634, w 5.6358, b -6.3110\n",
      "Epoch 750: Loss 378.4016, w 5.7330, b -6.7966\n",
      "Epoch 800: Loss 387.7976, w 5.8301, b -7.2822\n",
      "Epoch 850: Loss 397.3516, w 5.9272, b -7.7678\n",
      "Epoch 900: Loss 407.0635, w 6.0243, b -8.2535\n",
      "Epoch 950: Loss 416.9332, w 6.1214, b -8.7391\n",
      "Epoch 1000: Loss 426.9609, w 6.2186, b -9.2247\n",
      "Epoch 1050: Loss 437.1464, w 6.3157, b -9.7103\n",
      "Epoch 1100: Loss 447.4898, w 6.4128, b -10.1959\n",
      "Epoch 1150: Loss 457.9911, w 6.5099, b -10.6815\n",
      "Epoch 1200: Loss 468.6504, w 6.6070, b -11.1671\n",
      "Epoch 1250: Loss 479.4675, w 6.7042, b -11.6527\n",
      "Epoch 1300: Loss 490.4425, w 6.8013, b -12.1383\n",
      "Epoch 1350: Loss 501.5754, w 6.8984, b -12.6239\n",
      "Epoch 1400: Loss 512.8662, w 6.9955, b -13.1095\n",
      "Epoch 1450: Loss 524.3148, w 7.0926, b -13.5951\n",
      "Epoch 1500: Loss 535.9214, w 7.1898, b -14.0807\n",
      "Epoch 1550: Loss 547.6859, w 7.2869, b -14.5663\n",
      "Epoch 1600: Loss 559.6082, w 7.3840, b -15.0519\n",
      "Epoch 1650: Loss 571.6885, w 7.4811, b -15.5375\n",
      "Epoch 1700: Loss 583.9267, w 7.5783, b -16.0231\n",
      "Epoch 1750: Loss 596.3227, w 7.6754, b -16.5087\n",
      "Epoch 1800: Loss 608.8766, w 7.7725, b -16.9944\n",
      "Epoch 1850: Loss 621.5885, w 7.8696, b -17.4800\n",
      "Epoch 1900: Loss 634.4582, w 7.9667, b -17.9656\n",
      "Epoch 1950: Loss 647.4858, w 8.0639, b -18.4512\n",
      "\n",
      "训练结束：\n",
      "学习到的参数：w 8.1590, b -18.9271\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed(42)\n",
    "X = np.linspace(0, 10, 460) # # 生成X轴数据\n",
    "y = 3 * X + 2 + np.random.randn(460) * 2\n",
    "\n",
    "# 转换为矩阵形状（N, 1）\n",
    "X = X.reshape(-1, 1)\n",
    "y = y.reshape(-1, 1)\n",
    "\n",
    "# 初始化参数\n",
    "w = np.random.randn(1) # 随机初始化权重\n",
    "b = np.random.randn(1) # 随机初始化偏置\n",
    "\n",
    "# 超参数\n",
    "lr = 0.001 # 学习率\n",
    "epochs = 2000 # 迭代次数\n",
    "losses = [] # 损失函数值\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    y_pred = X.dot(w) + b\n",
    "    \n",
    "    # 计算损失（均方误差MSE）\n",
    "    loss = np.mean((y_pred - y) ** 2)\n",
    "    losses.append(loss)\n",
    "    \n",
    "    # 计算梯度\n",
    "    dw = np.mean(2 * (y_pred - y) * X) # 梯度计算 \n",
    "    db = np.mean(2 * (y_pred - y))\n",
    "    \n",
    "    # 更新参数\n",
    "    w -= lr * dw\n",
    "    b -= lr * db\n",
    "\n",
    "    if epoch % 50 == 0:\n",
    "        print(f\"Epoch {epoch}: Loss {loss:.4f}, w {w[0]:.4f}, b {b[0]:.4f}\")\n",
    "\n",
    "print(\"\\n训练结束：\")\n",
    "print(f\"学习到的参数：w {w[0]:.4f}, b {b[0]:.4f}\")\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.plot(losses)\n",
    "plt.title(\"Loss Curve\") # Cost Curve // 损失曲线\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "\n",
    "# 拟合效果\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(X, y, label=\"Real Data\")\n",
    "plt.plot(X, X.dot(w) + b, color=\"red\", linewidth=2, label=\"Prediction\")\n",
    "plt.title(\"Fitting Result\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "920bd653",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 32.7041\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# 读取数据\n",
    "data = pd.read_csv('沪深300-460_date.csv')\n",
    "data = data.dropna() # 处理缺失值\n",
    "data = data.drop_duplicates() # 处理重复值\n",
    "data = data.sort_values(by='date') # 按日期排序\n",
    "\n",
    "# 划分数据集为训练集和测试集\n",
    "X = data.drop(['date', 'close'], axis=1)\n",
    "y = data['close']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_pred) # 计算均方误差 // 计算 RMSE\n",
    "rmse = np.sqrt(mse) # 计算 RMSE\n",
    "print(f\"RMSE: {rmse:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "94c04b26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           date    close\n",
      "0    2005-01-04   982.79\n",
      "1    2005-01-19   967.21\n",
      "2    2005-02-03   993.22\n",
      "3    2005-03-01  1035.93\n",
      "4    2005-03-16  1003.07\n",
      "..          ...      ...\n",
      "455  2025-07-10  4010.02\n",
      "456  2025-07-25  4127.16\n",
      "457  2025-08-11  4122.51\n",
      "458  2025-08-26  4452.59\n",
      "459  2025-09-08  4462.98\n",
      "\n",
      "[460 rows x 2 columns]\n",
      "均方误差 MSE: 62701.71\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测下一天收盘价: 4145.60\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from xgboost import XGBRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 中文显示\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "\n",
    "# ============ 1. 准备数据 ============\n",
    "# 示例数据（日期, 收盘价）\n",
    "data = pd.read_csv('沪深300-460_date.csv')\n",
    "data = data[[\"date\", \"close\"]]\n",
    "print(data)\n",
    "\n",
    "df = pd.DataFrame(data, columns=[\"date\", \"close\"])\n",
    "\n",
    "# 特征工程：用前1天的收盘价预测当天收盘价\n",
    "df[\"prev_close\"] = df[\"close\"].shift(1)\n",
    "df = df.dropna()\n",
    "\n",
    "X = df[[\"prev_close\"]]   # 特征\n",
    "y = df[\"close\"]          # 目标值\n",
    "\n",
    "# ============ 2. 划分训练/测试集 ============\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=False)\n",
    "\n",
    "# ============ 3. 定义 & 训练模型 ============\n",
    "model = XGBRegressor(\n",
    "    n_estimators=200,    # 树的数量\n",
    "    learning_rate=0.05,  # 学习率\n",
    "    max_depth=3,         # 每棵树的深度\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# ============ 4. 预测 ============\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# ============ 5. 评估模型 ============\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f\"均方误差 MSE: {mse:.2f}\")\n",
    "\n",
    "# ============ 6. 可视化 ============\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.plot(df[\"date\"].iloc[-len(y_test):], y_test, label=\"真实收盘价\")\n",
    "plt.plot(df[\"date\"].iloc[-len(y_test):], y_pred, label=\"预测收盘价\", linestyle=\"--\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend()\n",
    "plt.title(\"XGBoost 股票收盘价预测\")\n",
    "plt.show()\n",
    "\n",
    "# ============ 7. 预测未来一天 ============\n",
    "last_close = df[\"close\"].iloc[-1]   # 最后一天收盘价\n",
    "tomorrow_pred = model.predict(np.array([[last_close]]))\n",
    "print(f\"预测下一天收盘价: {tomorrow_pred[0]:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "125591c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           date    close\n",
      "0    2005-01-04   982.79\n",
      "1    2005-01-19   967.21\n",
      "2    2005-02-03   993.22\n",
      "3    2005-03-01  1035.93\n",
      "4    2005-03-16  1003.07\n",
      "..          ...      ...\n",
      "455  2025-07-10  4010.02\n",
      "456  2025-07-25  4127.16\n",
      "457  2025-08-11  4122.51\n",
      "458  2025-08-26  4452.59\n",
      "459  2025-09-08  4462.98\n",
      "\n",
      "[460 rows x 2 columns]\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "XGBModel.fit() got an unexpected keyword argument 'early_stopping_rounds'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[19]\u001b[39m\u001b[32m, line 58\u001b[39m\n\u001b[32m     56\u001b[39m \u001b[38;5;66;03m# 8. 训练 XGBoost（预测回报）\u001b[39;00m\n\u001b[32m     57\u001b[39m model = XGBRegressor(n_estimators=\u001b[32m500\u001b[39m, learning_rate=\u001b[32m0.05\u001b[39m, max_depth=\u001b[32m4\u001b[39m, random_state=\u001b[32m42\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m58\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meval_set\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mearly_stopping_rounds\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meval_metric\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mrmse\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# 20轮内没有提升则停止 // early_stopping_rounds 早停机制\u001b[39;00m\n\u001b[32m     60\u001b[39m \u001b[38;5;66;03m# 9. 预测并还原为价格\u001b[39;00m\n\u001b[32m     61\u001b[39m pred_ret = model.predict(X_test)\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\anaconda3\\envs\\main\\Lib\\site-packages\\xgboost\\core.py:729\u001b[39m, in \u001b[36mrequire_keyword_args.<locals>.throw_if.<locals>.inner_f\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    727\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig.parameters, args):\n\u001b[32m    728\u001b[39m     kwargs[k] = arg\n\u001b[32m--> \u001b[39m\u001b[32m729\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[31mTypeError\u001b[39m: XGBModel.fit() got an unexpected keyword argument 'early_stopping_rounds'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from xgboost import XGBRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 中文显示\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "\n",
    "# ============ 1. 准备数据 ============\n",
    "# 示例数据（日期, 收盘价）\n",
    "data = pd.read_csv('沪深300-460_date.csv')\n",
    "data = data[[\"date\", \"close\"]]\n",
    "print(data)\n",
    "\n",
    "df = pd.DataFrame(data, columns=[\"date\", \"close\"])\n",
    "\n",
    "# 1. 基本处理\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df = df.sort_values('date').reset_index(drop=True)\n",
    "\n",
    "# 2. 特征工程：滞后价、滞后收益、均线\n",
    "df['ret'] = df['close'].pct_change()   # 当日回报\n",
    "for lag in range(1, 6):                # 前1..5天的价格及回报\n",
    "    df[f'lag_close_{lag}'] = df['close'].shift(lag)\n",
    "    df[f'lag_ret_{lag}'] = df['ret'].shift(lag)\n",
    "\n",
    "df['ma5'] = df['close'].rolling(5).mean()\n",
    "df['ma10'] = df['close'].rolling(10).mean()\n",
    "df['std5'] = df['close'].rolling(5).std()\n",
    "\n",
    "# 3. 目标：下一天的回报 (next_return)\n",
    "df['target_ret'] = df['close'].shift(-1) / df['close'] - 1\n",
    "\n",
    "# 4. 丢掉有 NaN 的行\n",
    "df = df.dropna().reset_index(drop=True)\n",
    "\n",
    "# 5. 准备 X, y\n",
    "feature_cols = [f'lag_close_{i}' for i in range(1,6)] + \\\n",
    "               [f'lag_ret_{i}' for i in range(1,6)] + ['ma5','ma10','std5']\n",
    "X = df[feature_cols]\n",
    "y = df['target_ret']\n",
    "\n",
    "# 6. 时间序列切分（最后 20% 做测试）\n",
    "test_ratio = 0.20\n",
    "test_size = int(len(df) * test_ratio)\n",
    "X_train, X_test = X.iloc[:-test_size], X.iloc[-test_size:]\n",
    "y_train, y_test = y.iloc[:-test_size], y.iloc[-test_size:]\n",
    "\n",
    "# 7. 基线（naive）：预测明天价格 = 今天价格 -> 明天回报 = 0\n",
    "#    先把真实价格和预测价格都算出来，便于直接比较 MSE（price）\n",
    "true_price = X_test['lag_close_1'] * (1 + y_test)       # 真实的 next price\n",
    "baseline_pred_price = X_test['lag_close_1']             # naive: tomorrow = today\n",
    "\n",
    "# 8. 训练 XGBoost（预测回报）\n",
    "model = XGBRegressor(n_estimators=500, learning_rate=0.05, max_depth=4, random_state=42)\n",
    "model.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=20, eval_metric='rmse', verbose=False) # 20轮内没有提升则停止 // early_stopping_rounds 早停机制\n",
    "\n",
    "# 9. 预测并还原为价格\n",
    "pred_ret = model.predict(X_test)\n",
    "pred_price = X_test['lag_close_1'] * (1 + pred_ret)\n",
    "\n",
    "# 10. 评估（price 层面的 MSE/MAE，与基线对比）\n",
    "baseline_mse = mean_squared_error(true_price, baseline_pred_price)\n",
    "baseline_mae = mean_absolute_error(true_price, baseline_pred_price)\n",
    "mse = mean_squared_error(true_price, pred_price)\n",
    "mae = mean_absolute_error(true_price, pred_price)\n",
    "\n",
    "print(\"样本数（训练/测试）:\", len(X_train), \"/\", len(X_test))\n",
    "print(\"Baseline (naive) price MSE, MAE:\", baseline_mse, baseline_mae)\n",
    "print(\"XGBoost model    price MSE, MAE:\", mse, mae)\n",
    "\n",
    "# 11. 快速看看最后几天的真实 vs 预测\n",
    "cmp = pd.DataFrame({\n",
    "    'date': df['date'].iloc[-len(y_test):].values,\n",
    "    'today_close': X_test['lag_close_1'].values,\n",
    "    'true_next_close': true_price.values,\n",
    "    'pred_next_close': pred_price\n",
    "})\n",
    "print(cmp.tail(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02e03f6b",
   "metadata": {},
   "outputs": [],
   "source": []
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